Diagnosing linearity for multiple regression model I have the following model in R to estimate the change in mosquito abundance each year when controlling for climate variables: 
modela <- lm(count_trapnights ~ temp + rain + year, data=wnv)
summary(modela)
I am running regression diagnostics and my RVF plot (shown below) looks like it may have some slight curvature. I'm not really familiar with what constitutes a clear violation of the linearity assumption. Would this be acceptable or does this violate the linearity assumption? Is there any way to quantify the violation with some kind of cut-off value? 
Non-transformed dependent variable:
 
Following dietervdf's suggestion, Box-cox transformed dependent variable: 

 A: I would build a different model. (I'm not entirely sure what the research question is though). How did you decide on the model? Did you just performed linear regression on all possible predictors and then selected the significant ones? Which are the possible predictors?
If you build the regression model step by step, you have more control on which variable should be added to the model, and in what way. I would keep the Box-Cox transformation anyway. It helps reducing skewness of the residuals.
lm.fit <- lm(count_trapnights ~ year + temp + rain, data=wnv)    
bc <- boxcox(lm.fit)
lambda <- bc$x[which.max(bc$y)]
wnv$bc.count_trapnights <- (wnv$count_trapnights^lambda-1)/lambda



*

*Looking at the primary variable of interest (year)
lm.fit.bc <- lm(bc.count_trapnights ~ year, data=wnv)
plot(lm.fit.bc)

You will notice that all assumptions seem satisfied.

*How will we add rain to the model? 
You can use an added-variable plot to test this out.
lm.fit.t <- lm(rain ~ year, data=wnv)
plot(lm.fit.bc$residuals ~ lm.fit.t$residuals)
lines(lowess(lm.fit.bc$residuals ~ lm.fit.t$residuals))

This graph suggests a linear relation.

*Lets use the same method to decide on how to add temp
First, lets define the current regression model.
lm.fit.bc1 <- lm(bc.count_trapnights ~ year + rain, data=wnv)
plot(lm.fit.bc1)

Now the added-variable plot
lm.fit.t <- lm(temp ~ year + rain, data=wnv)
plot(lm.fit.bc1$residuals ~ lm.fit.t$residuals)
lines(lowess(lm.fit.bc1$residuals ~ lm.fit.t$residuals))

This plot seems to suggest a different way to add temp. I've tried temp^2 and exp(temp). The latter seems to work the best.

*The full model is now:
lm.fit.full <- lm(bc.count_trapnights ~ year + rain + exp(temp), data=wnv)

With the following residual plot.

Keep in mind that I'm not an expert on regression. It's just some ideas from an statistics enthousiast ;)
